Metformin normalizes mitochondrial function to delay astrocyte senescence in a mouse model of Parkinson's disease through Mfn2-cGAS signaling.

BACKGROUND: Senescent astrocytes play crucial roles in age-associated neurodegenerative diseases, including Parkinson's disease (PD). Metformin, a drug widely used for treating diabetes, exerts longevity effects and neuroprotective activities. However, its effect on astrocyte senescence in PD remains to be defined. METHODS: Long culture-induced replicative senescence model and 1-methyl-4-phenylpyridinium/α-synuclein aggregate-induced premature senescence model, and a mouse model of PD were used to investigate the effect of metformin on astrocyte senescence in vivo and in vitro. Immunofluorescence staining and flow cytometric analyses were performed to evaluate the mitochondrial function. We stereotactically injected AAV carrying GFAP-promoter-cGAS-shRNA to mouse substantia nigra pars compacta regions to specifically reduce astrocytic cGAS expression to clarify the potential molecular mechanism by which metformin inhibited the astrocyte senescence in PD. RESULTS: We showed that metformin inhibited the astrocyte senescence in vitro and in PD mice. Mechanistically, metformin normalized mitochondrial function to reduce mitochondrial DNA release through mitofusin 2 (Mfn2), leading to inactivation of cGAS-STING, which delayed astrocyte senescence and prevented neurodegeneration. Mfn2 overexpression in astrocytes reversed the inhibitory role of metformin in cGAS-STING activation and astrocyte senescence. More importantly, metformin ameliorated dopamine neuron injury and behavioral deficits in mice by reducing the accumulation of senescent astrocytes via inhibition of astrocytic cGAS activation. Deletion of astrocytic cGAS abolished the suppressive effects of metformin on astrocyte senescence and neurodegeneration. CONCLUSIONS: This work reveals that metformin delays astrocyte senescence via inhibiting astrocytic Mfn2-cGAS activation and suggest that metformin is a promising therapeutic agent for age-associated neurodegenerative diseases.

b-MAR: bidirectional artifact representations learning framework for metal artifact reduction in dental CBCT.

OBJECTIVE: Metal artifacts in computed tomography (CT) images hinder diagnosis and treatment significantly. Specifically, dental cone-beam computed tomography (Dental CBCT) images are seriously contaminated by metal artifacts due to the widespread use of low tube voltages and the presence of various high-attenuation materials in dental structures. Existing supervised metal artifact reduction (MAR) methods mainly learn the mapping of artifact-affected images to clean images, while ignoring the modeling of the metal artifact generation process. Therefore, we propose the bidirectional artifact representations learning framework to adaptively encode metal artifacts caused by various dental implants and model the generation and elimination of metal artifacts, thereby improving MAR performance. Approach. we introduce an efficient artifact encoder to extract multi-scale representations of metal artifacts from artifact-affected images. These extracted metal artifact representations are then bidirectionally embedded into both the metal artifact generator and the metal artifact eliminator, which can simultaneously improve the performance of artifact removal and artifact generation. The artifact eliminator learns artifact removal in a supervised manner, while the artifact generator learns artifact generation in an adversarial manner. To further improve the performance of the bidirectional task networks, we propose artifact consistency loss to align the consistency of images generated by the eliminator and the generator with or without embedding artifact representations. Main results. To validate the effectiveness of our algorithm, experiments are conducted on simulated and clinical datasets containing various dental metal morphologies. Quantitative metrics are calculated to evaluate the results of the simulation tests,which demonstrate b-MAR improvements of > 1.4131 dB in PSNR, > 0.3473 HU decrements in RMSE, and > 0.0025 promotion in SSIM over the current state-of-the-art MAR methods. All results indicate that the proposed b-MAR method can remove artifacts caused by various metal morphologies and restore the structural integrity of dental tissues effectively. Significance. The proposed b-MAR method strengthens the joint learning of the artifact removal process and the artifact generation process by bidirectionally embedding artifact representations, thereby improving the model's artifact removal performance. Compared with other comparison methods, b-MAR can robustly and effectively correct metal artifacts in dental CBCT images caused by different dental metals.

Multiscale and multiperception feature learning for pancreatic lesion detection based on noncontrast CT.

Pancreatic cancer is one of the most malignant tumours, demonstrating a poor prognosis and nearly identically high mortality and morbidity, mainly because of the difficulty of early diagnosis and timely treatment for localized stages. It is clinically important to develop a noncontrast CT (NCCT)-based pancreatic lesion detection model that could serve as an intelligent tool for diagnosing pancreatic cancer early. However, abdominal NCCT presents low contrast intensities for the pancreas and its lesions and complex anatomical structures, developing such a model is challenging. Therefore, we design a multiscale and multiperception (MSMP) feature learning network with ResNet50 coupled with a feature pyramid network (FPN) as the backbone for strengthening feature expressions. We added multiscale atrous convolutions to expand different receptive fields, contextual attention to perceive contextual information, and channel and spatial attention to focus on important channels and spatial regions, respectively. The MSMP network then acts as a feature extractor for proposing an NCCT-based pancreatic lesion detection model with image patches covering the pancreas as its input; Faster R-CNN is employed as the detection method for accurately detecting pancreatic lesions. By using the new MSMP network as a feature extractor, our model outperforms the conventional object detection algorithms in terms of the recall (75.40% and 90.95%), precision (40.84% and 68.21%), F1 score (52.98% and 77.96%), F2 score (64.48% and 85.26%) and Ap50 (53.53% and 70.14%) at the image and patient levels, respectively. The good performance of our new model implies that MSMP can mine NCCT imaging features for detecting pancreatic lesions from complex backgrounds well. The proposed detection model is expected to be developed as an intelligent method for the early detection of pancreatic cancer.

Evaluation of low-dose computed tomography reconstruction using spatial-radon domain total generalized variation regularization.

The x-ray radiation dose in computed tomography (CT) examination has been a major concern for patients. Lowing the tube current and exposure time in data acquisition is a straightforward and cost-effective strategy to reduce the x-ray radiation dose. However, this will inevitably increase the noise fluctuations in measured projection data, and the corresponding CT image quality will be severely degraded if noise suppression is not performed during image reconstruction. To reconstruct high-quality low-dose CT image, we present a spatial-radon domain total generalized variation (SRDTGV) regularization for statistical iterative reconstruction (SIR) based on penalized weighted least-squares (PWLS) principle, which is called PWLS-SRDTGV for simplicity. The presented PWLS-SRDTGV model can simultaneously reconstruct high-quality CT image in space domain and its corresponding projection in radon domain. An efficient split Bregman algorithm was applied to minimize the cost function of the proposed reconstruction model. Qualitative and quantitative studies were performed to evaluate the effectiveness of the PWLS-SRDTGV image reconstruction algorithm using a digital 3D XCAT phantom and an anthropomorphic torso phantom. The experimental results demonstrate that PWLS-SRDTGV algorithm achieves notable gains in noise reduction, streak artifact suppression, and edge preservation compared with competing reconstruction approaches.

Gastric emptying of a glucose drink is predictive of the glycaemic response to oral glucose and mixed meals, but unrelated to antecedent glycaemic control, in type 2 diabetes.

BACKGROUND: Gastric emptying (GE), with wide inter-individual but lesser intra-individual variations, is a major determinant of postprandial glycaemia in health and type 2 diabetes (T2D). However, it is uncertain whether GE of a carbohydrate-containing liquid meal is predictive of the glycaemic response to physiological meals, and whether antecedent hyperglycaemia influences GE in T2D. We evaluated the relationships of (i) the glycaemic response to both a glucose drink and mixed meals with GE of a 75 g glucose drink, and (ii) GE of a glucose drink with antecedent glycaemic control, in T2D. METHODS: Fifty-five treatment-naive Chinese adults with newly diagnosed T2D consumed standardised meals at breakfast, lunch and dinner with continuous interstitial glucose monitoring. On the subsequent day, a 75 g glucose drink containing 150 mg 13C-acetate was ingested to assess GE (breath test) and plasma glucose response. Serum fructosamine and HbA1c were also measured. RESULTS: Plasma glucose incremental area under the curve (iAUC) within 2 hours after oral glucose was related inversely to the gastric half-emptying time (T50) (r = -0.34, P = 0.012). The iAUCs for interstitial glucose within 2 hours after breakfast (r = -0.34, P = 0.012) and dinner (r = -0.28, P = 0.040) were also related inversely to the T50 of oral glucose. The latter, however, was unrelated to antecedent fasting plasma glucose, 24-hour mean interstitial glucose, serum fructosamine, or HbA1c. CONCLUSIONS: In newly diagnosed, treatment-naive, Chinese with T2D, GE of a 75 g glucose drink predicts the glycaemic response to both a glucose drink and mixed meals, but is not influenced by spontaneous short-, medium- or longer-term elevation in glycaemia.

Decreased glucagon in diabetic peripheral neuropathy patients with long duration type 2 diabetes.

OBJECTIVE: The aim of this study was to investigate the association of fasting C-peptide and glucagon with diabetic peripheral neuropathy (DPN) in patients with type 2 diabetes (T2DM). METHODS: A comprehensive evaluation was conducted on 797 patients with T2DM to assess the various risk factors affecting DPN. The subjects were categorized into short duration and long duration group according to the duration of diabetes with a threshold of 10 years. Logistic regression analysis was employed to examine the association between DPN and islet function, as well as other parameters. Receiver operating characteristic curve analysis was performed to evaluate the predictive capability of glucagon. RESULTS: The fasting C-peptide levels were significantly lower in the DPN patients with short duration of diabetes, but lost significance in the long duration group. Conversely, a decreased level of glucagon was only observed in DPN patients with long duration of diabetes. For the group with long duration of diabetes, glucagon was the sole risk factor associated with DPN. The receiver operating characteristic curve analysis revealed that glucagon in the long duration group exhibited a moderate area under the curve of 0.706. CONCLUSIONS: The serum glucagon levels in T2DM patients with DPN exhibited bidirectional changes based on the duration of diabetes. Decreased glucagon was associated with DPN in T2DM patients with long duration of diabetes.

Diabetes Pharmacotherapy and its effects on the Skeletal Muscle Energy Metabolism.

The disorders of skeletal muscle metabolism in patients with Type 2 diabetes mellitus (T2DM), such as mitochondrial defection and glucose transporters (GLUTs) translocation dysfunctions, are not uncommon. Therefore, when anti-diabetic drugs were used in various chronic diseases associated with hyperglycemia, the impact on skeletal muscle should not be ignored. However, current studies mainly focus on muscle mass rather than metabolism or functions. Anti-diabetic drugs might have a harmful or beneficial impact on skeletal muscle. In this review, we summarize the upto- date studies on the effects of anti-diabetic drugs and some natural compounds on skeletal muscle metabolism, focusing primarily on emerging data from pre-clinical to clinical studies. Given the extensive use of anti-diabetic drugs and the common sarcopenia, a better understanding of energy metabolism in skeletal muscle deserves attention in future studies.

DDT-Net: Dose-Agnostic Dual-Task Transfer Network for Simultaneous Low-Dose CT Denoising and Simulation.

Deep learning (DL) algorithms have achieved unprecedented success in low-dose CT (LDCT) imaging and are expected to be a new generation of CT reconstruction technology. However, most DL-based denoising models often lack the ability to generalize to unseen dose data. And they only learn the posterior distribution of latent normal-dose CT (NDCT) images conditioned on observed LDCT images in the traditional maximum a posteriori (MAP) framework, while ignoring the noise generation process of LDCT images. Moreover, most simulation tools for LDCT typically operate on proprietary projection data, which is generally not accessible without an established collaboration with CT manufacturers. To alleviate these issues, in this work, we propose a dose-agnostic dual-task transfer network, termed DDT-Net, for simultaneous LDCT denoising and simulation. Concretely, the dual-task learning module is constructed to integrate the LDCT denoising and simulation tasks into a unified optimization framework by learning the joint distribution of LDCT and NDCT data. We approximate the joint distribution of continuous dose level data by training DDT-Net with discrete dose data, which can be generalized to denoising and simulation of unseen dose data. In particular, the mixed-dose training strategy adopted by DDT-Net can promote the denoising performance of lower-dose data. The paired dataset simulated by DDT-Net can be used for data augmentation to further restore the tissue texture of LDCT images. Experimental results on synthetic data and clinical data show that the proposed DDT-Net outperforms competing methods in terms of denoising and generalization performance at unseen dose data, and it also provides a simulation tool that can quickly simulate realistic LDCT images at arbitrary dose levels.

A model-based MR parameter mapping network robust to substantial variations in acquisition settings.

Deep learning methods show great potential for the efficient and precise estimation of quantitative parameter maps from multiple magnetic resonance (MR) images. Current deep learning-based MR parameter mapping (MPM) methods are mostly trained and tested using data with specific acquisition settings. However, scan protocols usually vary with centers, scanners, and studies in practice. Thus, deep learning methods applicable to MPM with varying acquisition settings are highly required but still rarely investigated. In this work, we develop a model-based deep network termed MMPM-Net for robust MPM with varying acquisition settings. A deep learning-based denoiser is introduced to construct the regularization term in the nonlinear inversion problem of MPM. The alternating direction method of multipliers is used to solve the optimization problem and then unrolled to construct MMPM-Net. The variation in acquisition parameters can be addressed by the data fidelity component in MMPM-Net. Extensive experiments are performed on R2 mapping and R1 mapping datasets with substantial variations in acquisition settings, and the results demonstrate that the proposed MMPM-Net method outperforms other state-of-the-art MR parameter mapping methods both qualitatively and quantitatively.

Unveiling camouflaged and partially occluded colorectal polyps: Introducing CPSNet for accurate colon polyp segmentation.

BACKGROUND: Segmenting colorectal polyps presents a significant challenge due to the diverse variations in their size, shape, texture, and intricate backgrounds. Particularly demanding are the so-called "camouflaged" polyps, which are partially concealed by surrounding tissues or fluids, adding complexity to their detection. METHODS: We present CPSNet, an innovative model designed for camouflaged polyp segmentation. CPSNet incorporates three key modules: the Deep Multi-Scale-Feature Fusion Module, the Camouflaged Object Detection Module, and the Multi-Scale Feature Enhancement Module. These modules work collaboratively to improve the segmentation process, enhancing both robustness and accuracy. RESULTS: Our experiments confirm the effectiveness of CPSNet. When compared to state-of-the-art methods in colon polyp segmentation, CPSNet consistently outperforms the competition. Particularly noteworthy is its performance on the ETIS-LaribPolypDB dataset, where CPSNet achieved a remarkable 2.3% increase in the Dice coefficient compared to the Polyp-PVT model. CONCLUSION: In summary, CPSNet marks a significant advancement in the field of colorectal polyp segmentation. Its innovative approach, encompassing multi-scale feature fusion, camouflaged object detection, and feature enhancement, holds considerable promise for clinical applications.

Semi-supervised iterative adaptive network for low-dose CT sinogram recovery.

Background.Concern has been expressed regarding the risk of carcinogenesis from medical computed tomography (CT) radiation. Lowering radiation in CT without appropriate modifications often leads to severe noise-induced artifacts in the images. The utilization of deep learning (DL) techniques has achieved promising reconstruction performance in low-dose CT (LDCT) imaging. However, most DL-based algorithms require the pre-collection of a large set of image pairs (low-dose/standard-dose) and the training of networks in an end-to-end supervised manner. Meanwhile, securing such a large volume of paired, well-registered training data in clinical practice is challenging. Moreover, these algorithms often overlook the potential to utilize the abundant information in a large collection of LDCT-only images/sinograms.Methods.In this paper, we introduce a semi-supervised iterative adaptive network (SIA-Net) for LDCT imaging, utilizing both labeled and unlabeled sinograms in a cohesive network framework, integrating supervised and unsupervised learning processes. Specifically, the supervised process captures critical features (i.e. noise distribution and tissue characteristics) latent in the paired sinograms, while the unsupervised process effectively learns these features in the unlabeled low-dose sinograms, employing a conventional weighted least-squares model with a regularization term. Furthermore, the SIA-Net method is designed to adaptively transfer the learned feature distribution from the supervised to the unsupervised process, thereby obtaining a high-fidelity sinogram through iterative adaptive learning. Finally, high-quality CT images can be reconstructed from the refined sinogram using the filtered back-projection algorithm.Results.Experimental results on two clinical datasets indicate that the proposed SIA-Net method achieves competitive performance in terms of noise reduction and structure preservation in LDCT imaging, when compared to traditional supervised learning methods.

Individualized intensive insulin therapy of diabetes: Not only the goal, but also the time.

Intensive insulin therapy has been extensively used to control blood glucose levels because of its ability to reduce the risk of chronic complications of diabetes. According to current guidelines, intensive glycemic control requires individualized glucose goals rather than as low as possible. During intensive therapy, rapid blood glucose reduction can aggravate microvascular and macrovascular complications, and prolonged overuse of insulin can lead to treatment-induced neuropathy and retinopathy, hypoglycemia, obesity, lipodystrophy, and insulin antibody syndrome. Therefore, we need to develop individualized hypoglycemic plans for patients with diabetes, including the time required for blood glucose normalization and the duration of intensive insulin therapy, which deserves further study.

Efficacy and safety of tirzepatide in people with type 2 diabetes by baseline body mass index: An exploratory subgroup analysis of SURPASS-AP-Combo.

AIMS: To assess the efficacy and safety of tirzepatide versus insulin glargine in people with type 2 diabetes (T2D) by baseline body mass index (BMI). MATERIALS AND METHODS: Participants with T2D from the Phase 3 SURPASS-AP-Combo trial (NCT04093752) were categorized into three BMI subgroups (normal weight [<25 kg/m2 ], overweight [≥25 and <30 kg/m2 ], and obese [≥30 kg/m2 ]) according to World Health Organization criteria. Exploratory outcomes including glycaemic control, body weight, cardiometabolic risk, and safety were compared among three tirzepatide doses (5, 10 or 15 mg) and insulin glargine. RESULTS: Of 907 participants, 235 (25.9%) had a BMI <25 kg/m2 , 458 (50.5%) a BMI ≥25 to <30 kg/m2 , and 214 (23.6%) a BMI ≥30 kg/m2 at baseline. At Week 40, all tirzepatide doses led to a greater reduction in mean glycated haemoglobin (HbA1c; -2.0% to -2.8% vs. -0.8% to -1.0%, respectively) and percent change in body weight (-5.5% to -10.8% vs. 1.0% to 2.5%, respectively) versus insulin glargine, across the BMI subgroups. Compared with insulin glargine, a higher proportion of tirzepatide-treated participants achieved treatment goals for HbA1c and body weight reduction. Improvements in other cardiometabolic indicators were also observed with tirzepatide across all the BMI subgroups. The safety profile of tirzepatide was similar across all subgroups by BMI. The most frequent adverse events with tirzepatide were gastrointestinal-related events and decreased appetite, with relatively few events leading to treatment discontinuation. CONCLUSIONS: In participants with T2D, regardless of baseline BMI, treatment with tirzepatide resulted in statistically significant and clinically meaningful glycaemic reductions and body weight reductions compared with insulin glargine, with a safety profile consistent with previous reports.

Breaking boundaries in radiology: redefining AI diagnostics via raw data ahead of reconstruction.

Objective.In the realm of utilizing artificial intelligence (AI) for medical image analysis, the paradigm of 'signal-image-knowledge' has remained unchanged. However, the process of 'signal to image' inevitably introduces information distortion, ultimately leading to irrecoverable biases in the 'image to knowledge' process. Our goal is to skip reconstruction and build a diagnostic model directly from the raw data (signal).Approach. This study focuses on computed tomography (CT) and its raw data (sinogram) as the research subjects. We simulate the real-world process of 'human-signal-image' using the workflow 'CT-simulated data- reconstructed CT,' and we develop a novel AI predictive model directly targeting raw data (RCTM). This model comprises orientation, spatial, and global analysis modules, embodying the fusion of local to global information extraction from raw data. We selected 1994 patients with retrospective cases of solid lung nodules and modeled different types of data.Main results. We employed predefined radiomic features to assess the diagnostic feature differences caused by reconstruction. The results indicated that approximately 14% of the features had Spearman correlation coefficients below 0.8. These findings suggest that despite the increasing maturity of CT reconstruction algorithms, they still introduce perturbations to diagnostic features. Moreover, our proposed RCTM achieved an area under the curve (AUC) of 0.863 in the diagnosis task, showcasing a comprehensive superiority over models constructed from secondary reconstructed CTs (0.840, 0.822, and 0.825). Additionally, the performance of RCTM closely resembled that of models constructed from original CT scans (0.868, 0.878, and 0.866).Significance. The diagnostic and therapeutic approach directly based on CT raw data can enhance the precision of AI models and the concept of 'signal-to-image' can be extended to other types of imaging. AI diagnostic models tailored to raw data offer the potential to disrupt the traditional paradigm of 'signal-image-knowledge', opening up new avenues for more accurate medical diagnostics.

Super Resolution Dual-Energy Cone-Beam CT Imaging With Dual-Layer Flat-Panel Detector.

In flat-panel detector (FPD) based cone-beam computed tomography (CBCT) imaging, the native receptor array is usually binned into a smaller matrix size. By doing so, the signal readout speed could be increased by 4-9 times at the expense of a spatial resolution loss of 50%-67%. Clearly, such manipulation poses a key bottleneck in generating high spatial and high temporal resolution CBCT images at the same time. In addition, the conventional FPD is also difficult in generating dual-energy CBCT images. In this paper, we propose an innovative super resolution dual-energy CBCT imaging method, named as suRi, based on dual-layer FPD (DL-FPD) to overcome these aforementioned difficulties at once. With suRi, specifically, a 1D or 2D sub-pixel (half pixel in this study) shifted binning is applied instead of the conventionally aligned binning to double the spatial sampling rate during the dual-energy data acquisition. As a result, the suRi approach provides a new strategy to enable high spatial resolution CBCT imaging while at high readout speed. Moreover, a penalized likelihood material decomposition algorithm is developed to directly reconstruct the high resolution bases from these dual-energy CBCT projections containing sub-pixel shifts. Numerical and physical experiments are performed to validate this newly developed suRi method with phantoms and biological specimen. Results demonstrate that suRi can significantly improve the spatial resolution of the CBCT image. We believe this developed suRi method would greatly enhance the imaging performance of the DL-FPD based dual-energy CBCT systems in future.

Efficacy and Safety of Mazdutide in Chinese Patients With Type 2 Diabetes: A Randomized, Double-Blind, Placebo-Controlled Phase 2 Trial.

OBJECTIVE: We conducted a randomized, double-blind, placebo-controlled phase 2 trial to evaluate the efficacy and safety of mazdutide, a once-weekly glucagon-like peptide 1 and glucagon receptor dual agonist, in Chinese patients with type 2 diabetes. RESEARCH DESIGN AND METHODS: Adults with type 2 diabetes inadequately controlled with diet and exercise alone or with stable metformin (glycated hemoglobin A1c [HbA1c] 7.0-10.5% [53-91 mmol/mol]) were randomly assigned to receive 3 mg mazdutide (n = 51), 4.5 mg mazdutide (n = 49), 6 mg mazdutide (n = 49), 1.5 mg open-label dulaglutide (n = 50), or placebo (n = 51) subcutaneously for 20 weeks. The primary outcome was change in HbA1c from baseline to week 20. RESULTS: Mean changes in HbA1c from baseline to week 20 ranged from -1.41% to -1.67% with mazdutide (-1.35% with dulaglutide and 0.03% with placebo; all P < 0.0001 vs. placebo). Mean percent changes in body weight from baseline to week 20 were dose dependent and up to -7.1% with mazdutide (-2.7% with dulaglutide and -1.4% with placebo). At week 20, participants receiving mazdutide were more likely to achieve HbA1c targets of <7.0% (53 mmol/mol) and ≤6.5% (48 mmol/mol) and body weight loss from baseline of ≥5% and ≥10% compared with placebo-treated participants. The most common adverse events with mazdutide included diarrhea (36%), decreased appetite (29%), nausea (23%), vomiting (14%), and hypoglycemia (10% [8% with placebo]). CONCLUSIONS: In Chinese patients with type 2 diabetes, mazdutide dosed up to 6 mg was generally safe and demonstrated clinically meaningful HbA1c and body weight reductions.

OSCNet: Orientation-Shared Convolutional Network for CT Metal Artifact Learning.

X-ray computed tomography (CT) has been broadly adopted in clinical applications for disease diagnosis and image-guided interventions. However, metals within patients always cause unfavorable artifacts in the recovered CT images. Albeit attaining promising reconstruction results for this metal artifact reduction (MAR) task, most of the existing deep-learning-based approaches have some limitations. The critical issue is that most of these methods have not fully exploited the important prior knowledge underlying this specific MAR task. Therefore, in this paper, we carefully investigate the inherent characteristics of metal artifacts which present rotationally symmetrical streaking patterns. Then we specifically propose an orientation-shared convolution representation mechanism to adapt such physical prior structures and utilize Fourier-series-expansion-based filter parametrization for modelling artifacts, which can finely separate metal artifacts from body tissues. By adopting the classical proximal gradient algorithm to solve the model and then utilizing the deep unfolding technique, we easily build the corresponding orientation-shared convolutional network, termed as OSCNet. Furthermore, considering that different sizes and types of metals would lead to different artifact patterns (e.g., intensity of the artifacts), to better improve the flexibility of artifact learning and fully exploit the reconstructed results at iterative stages for information propagation, we design a simple-yet-effective sub-network for the dynamic convolution representation of artifacts. By easily integrating the sub-network into the proposed OSCNet framework, we further construct a more flexible network structure, called OSCNet+, which improves the generalization performance. Through extensive experiments conducted on synthetic and clinical datasets, we comprehensively substantiate the effectiveness of our proposed methods. Code will be released at https://github.com/hongwang01/OSCNet.

Multi-Scale Tokens-Aware Transformer Network for Multi-Region and Multi-Sequence MR-to-CT Synthesis in a Single Model.

The superiority of magnetic resonance (MR)-only radiotherapy treatment planning (RTP) has been well demonstrated, benefiting from the synthesis of computed tomography (CT) images which supplements electron density and eliminates the errors of multi-modal images registration. An increasing number of methods has been proposed for MR-to-CT synthesis. However, synthesizing CT images of different anatomical regions from MR images with different sequences using a single model is challenging due to the large differences between these regions and the limitations of convolutional neural networks in capturing global context information. In this paper, we propose a multi-scale tokens-aware Transformer network (MTT-Net) for multi-region and multi-sequence MR-to-CT synthesis in a single model. Specifically, we develop a multi-scale image tokens Transformer to capture multi-scale global spatial information between different anatomical structures in different regions. Besides, to address the limited attention areas of tokens in Transformer, we introduce a multi-shape window self-attention into Transformer to enlarge the receptive fields for learning the multi-directional spatial representations. Moreover, we adopt a domain classifier in generator to introduce the domain knowledge for distinguishing the MR images of different regions and sequences. The proposed MTT-Net is evaluated on a multi-center dataset and an unseen region, and remarkable performance was achieved with MAE of 69.33 ± 10.39 HU, SSIM of 0.778 ± 0.028, and PSNR of 29.04 ± 1.32 dB in head & neck region, and MAE of 62.80 ± 7.65 HU, SSIM of 0.617 ± 0.058 and PSNR of 25.94 ± 1.02 dB in abdomen region. The proposed MTT-Net outperforms state-of-the-art methods in both accuracy and visual quality.

Alterations of outer retinal reflectivity in diabetic patients without clinically detectable retinopathy.

PURPOSE: This study aimed to investigate alterations of outer retinal reflectivity on spectral-domain optical coherence tomography (OCT) in diabetic patients without clinically detectable retinopathy (NDR). METHODS: In this retrospective study, 64 NDR patients and 71 controls were included. Relative reflectivity (RR) of the ellipsoid zone (EZ), photoreceptor outer segment (OS) and inner segment (IS), and outer nuclear layer (ONL) at the foveola and at 500 µm, 1000 µm, and 2000 µm nasal (N), temporal (T), superior (S), and inferior (I) to the foveola was measured by cross-line OCT and ImageJ. Retinal vessel densities (VD) in fovea, parafovea, and perifovea areas were detected by OCT angiography (OCTA). RESULTS: EZ RR in most retinal locations was significantly lower in NDR eyes compared to controls (all P < 0.05), except the foveola. Compared with controls, NDR eyes also displayed lower RR at N2000, T2000, S1000, and I1000 of OS, at S500 and I500 of IS, and at I500 of ONL (all P < 0.05). Negative correlations could be observed between retinal RR and diabetes duration, HbA1c, and best-corrected visual acuity (BCVA) (r = - 0.303 to - 0.452). Compared to controls, EZ, OS, and IS RR of the NDR eyes showed lower correlation coefficients with whole image SCP and DCP VD of parafovea and perifovea regions. CONCLUSION: Outer retinal reflectivity, along with the coefficients between retinal reflectivity and VD, is reduced in NDR patients and is correlated with diabetes duration, HbA1c, and BCVA. The reduction of outer retinal reflectivity may be a potential biomarker of early retinal alterations in diabetic patients.

Ultra-rapid lispro improved postprandial glucose control compared to insulin lispro in predominantly Chinese patients with type 1 diabetes: A prospective, randomized, double-blind phase 3 study.

AIMS: To investigate the efficacy and safety of ultra-rapid lispro (URLi) versus insulin lispro in predominantly Chinese patients with type 1 diabetes (T1D) in a prospective, randomized, double-blind, treat-to-target, phase 3 study. MATERIALS AND METHODS: Following a lead-in period, during which insulin glargine U-100 or insulin degludec U-100 was optimized, patients were randomly assigned (1:1) to URLi (n = 176) or insulin lispro (n = 178). The primary objective was to test the noninferiority of URLi to insulin lispro in glycaemic control (noninferiority margin = 0.4% for glycated haemoglobin [HbA1c] change from baseline to week 26), with testing for the superiority of URLi to insulin lispro with regard to 1- and 2-hour postprandial glucose (PPG) excursions during a mixed-meal tolerance test and HbA1c change at week 26 as the multiplicity-adjusted objectives. RESULTS: From baseline to week 26, HbA1c decreased by 0.21% and 0.28% with URLi and insulin lispro, respectively, with a least squares mean treatment difference of 0.07% (95% confidence interval -0.11 to 0.24; P = 0.467). URLi demonstrated smaller 1- and 2-hour PPG excursions at week 26 with least squares mean treatment differences of -1.0 mmol/L (-17.8 mg/dL) and -1.4 mmol/L (-25.5 mg/dL), respectively (p < 0.005 for both) versus insulin lispro. The safety profiles of URLi and insulin lispro were similar. CONCLUSIONS: In this study, URLi administered in a basal-bolus regimen demonstrated superiority to insulin lispro in controlling PPG excursions, with noninferiority of HbA1c control in predominantly Chinese patients with T1D.